SoroushVahidi/scidocs huggingface dataset:
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
(2001)

Stereo matching is one of the most active research areas in computer vision. While a large number of algorithms for stereo correspondence have been developed, relatively little work has been done on characterizing their performance. In this paper, we present a taxonomy of dense, two-frame stereo methods. Our taxonomy is designed to assess the different components and design decisions made in individual stereo algorithms. Using this taxonomy, we compare existing stereo methods and present experiments evaluating the performance of many different variants. In order to establish a common software platform and a collection of data sets for easy evaluation, we have designed a stand-alone, flexible C++ implementation that enables the evaluation of individual components and that can easily be extended to include new algorithms. We have also produced several new multi-frame stereo data sets with ground truth and are making both the code and data sets available on the Web. Finally, we include a comparative evaluation of a large set of today's best-performing stereo algorithms.
A Compact 90-degree Twist using Novel Ridged Waveguide for Integrated Waveguide Subsystems
(2006)

A compact waveguide twist using a single step of novel ridged structure for integrated waveguide subsystems is proposed. The cross-section of the ridged waveguide has the shape of two squares partially overlapped. The advantages of this configuration are the facilitation of manufacturing and the reduction of overall dimensions. In this paper, the broadband operation principle is described by using equivalent circuit, moreover the its design using HFSS and the experimental results are presented. In the result, the return loss in excess of 30dB could be obtained over 22 percent of frequency band without additional waveguide steps. The twist length is 0.22 times the wavelength of the standard rectangular waveguides
An active volumetric model for 3D reconstruction
(2005)

In this paper, we present an active volumetric model (AVM) for 3D reconstruction from multiple calibrated images of a scene. The AVM is a physically motivated 3D deformable model which shrinks actively under the influence of multiple simulated forces towards the real scene by throwing away some of its voxels. It provides a computational framework to integrate several constraints in an intelligible way. In the current work, we use three forces derived respectively from the smooth constraint, the compulsory silhouette constraint, and the color consistency constraint. Based on the composition of the 3 forces, our algorithm can significantly restrain holes and floating voxels, which plague voxel coloring algorithms, and produce precise and smooth models. We test our algorithm by experiments based on both synthetic and real data.
Effective Fashion Retrieval Based on Semantic Compositional Networks
(2018)

Typical approaches for fashion retrieval rank clothing images according to the similarity to a user-provided query image. Similarity is usually assessed by encoding images in terms of visual elements such as color, shape and texture. In this work, we proceed differently and consider that the semantics of an outfit is mainly comprised of environmental and cultural concepts such as occasion, style and season. Thus, instead of retrieving outfits using strict visual elements, we find semantically similar outfits that fall into similar clothing styles and are adequate for the same occasions and seasons. We propose a compositional approach for fashion retrieval by arguing that the semantics of an outfit can be recognised by their constituents (i.e., clothing items and accessories). Specifically, we present a semantic compositional network (Comp-Net) in which clothing items are detected from the image and the probability of each item is used to compose a vector representation for the outfit. Comp-Net employs a normalization layer so that weights are updated by taking into consideration the previously known co-occurrence patterns between clothing items. Further, Comp-Net minimizes a cost-sensitive loss function as errors have different costs depending on the clothing item that is misclassified. This results in a space in which semantically related outfits are placed next to each other, enabling to find semantically similar outfits that may not be visually similar. We designed an evaluation setup that takes into account the association between different styles, occasions and seasons, and show that our compositional approach significantly outperforms a variety of recently proposed baselines.
Using humans as sensors: An estimation-theoretic perspective
(2014)

The explosive growth in social network content suggests that the largest "sensor network" yet might be human. Extending the participatory sensing model, this paper explores the prospect of utilizing social networks as sensor networks, which gives rise to an interesting reliable sensing problem. In this problem, individuals are represented by sensors (data sources) who occasionally make observations about the physical world. These observations may be true or false, and hence are viewed as binary claims. The reliable sensing problem is to determine the correctness of reported observations. From a networked sensing standpoint, what makes this sensing problem formulation different is that, in the case of human participants, not only is the reliability of sources usually unknown but also the original data provenance may be uncertain. Individuals may report observations made by others as their own. The contribution of this paper lies in developing a model that considers the impact of such information sharing on the analytical foundations of reliable sensing, and embed it into a tool called Apollo that uses Twitter as a "sensor network" for observing events in the physical world. Evaluation, using Twitter-based case-studies, shows good correspondence between observations deemed correct by Apollo and ground truth.
Enhancing perceptual and attentional skills requires common demands between the action video games and transfer tasks
(2015)

Despite increasing evidence that shows action video game play improves perceptual and cognitive skills, the mechanisms of transfer are not well-understood. In line with previous work, we suggest that transfer is dependent upon common demands between the game and transfer task. In the current study, participants played one of four action games with varying speed, visual, and attentional demands for 20 h. We examined whether training enhanced performance for attentional blink, selective attention, attending to multiple items, visual search and auditory detection. Non-gamers who played the game (Modern Combat) with the highest demands showed transfer to tasks of attentional blink and attending to multiple items. The game (MGS Touch) with fewer attentional demands also decreased attentional blink, but to a lesser degree. Other games failed to show transfer, despite having many action game characteristics but at a reduced intensity. The results support the common demands hypothesis.
Psychosocial functioning and depression: distinguishing among antecedents, concomitants, and consequences.
(1988)

In this article we attempt to distinguish empirically between psycho-social variables that are concomitants of depression, and variables that may serve as antecedents or sequelae of this disorder. We review studies that investigated the relationship between depression and any of six psychosocial variables after controlling for the effects of concurrent depression. The six variables examined are attributional style, dysfunctional attitudes, personality, social support, marital distress, and coping style. The review suggests that whereas there is little evidence in adults of a cognitive vulnerability to clinical depression, disturbances in interpersonal functioning may be antecedents or sequelae of this disorder. Specifically, marital distress and low social integration appear to be involved in the etiology of depression, and introversion and interpersonal dependency are identified as enduring abnormalities in the functioning of remitted depressives. We attempt to integrate what is known about the relationships among these latter variables, suggest ways in which they may influence the development of depression, and outline specific issues to be addressed in future research.
Formal Verification With Frama-C: A Case Study in the Space Software Domain
(2016)

With the increasing importance of software in the aerospace field, as evidenced by its growing size and complexity, a rigorous and reliable software verification and validation process must be applied to ensure conformance with the strict requirements of this software. Although important, traditional validation activities such as testing and simulation can only provide a partial verification of behavior in critical real-time software systems, and thus, formal verification is an alternative to complement these activities. Two useful formal software verification approaches are deductive verification and abstract interpretation, which analyze programs statically to identify defects. This paper explores abstract interpretation and deductive verification by employing Frama-C's value analysis and Jessie plug-ins to verify embedded aerospace control software. The results indicate that both approaches can be employed in a software verification process to make software more reliable.
Validation of heart rate extraction using video imaging on a built-in camera system of a smartphone
(2012)

As a smartphone is becoming very popular and its performance is being improved fast, a smartphone shows its potential as a low-cost physiological measurement solution which is accurate and can be used beyond the clinical environment. Because cardiac pulse leads the subtle color change of a skin, a pulsatile signal which can be described as photoplethysmographic (PPG) signal can be measured through recording facial video using a digital camera. In this paper, we explore the potential that the reliable heart rate can be measured remotely by the facial video recorded using smartphone camera. First, using the front facing-camera of a smartphone, facial video was recorded. We detected facial region on the image of each frame using face detection, and yielded the raw trace signal from the green channel of the image. To extract more accurate cardiac pulse signal, we applied independent component analysis (ICA) to the raw trace signal. The heart rate was extracted using frequency analysis of the raw trace signal and the analyzed signal from ICA. The accuracy of the estimated heart rate was evaluated by comparing with the heart rate from reference electrocardiogram (ECG) signal. Finally, we developed FaceBEAT, an iPhone application for remote heart rate measurement, based on this study.
The minds of gods: A comparative study of supernatural agency
(2013)

The present work is the first study to systematically compare the minds of gods by examining some of the intuitive processes that guide how people reason about them. By examining the Christian god and the spirit-masters of the Tyva Republic, it first confirms that the consensus view of the Christian god's mind is one of omniscience with acute concern for interpersonal social behavior (i.e., moral behaviors) and that Tyvan spirit-masters are not as readily attributed with knowledge or concern of moral information. Then, it reports evidence of a moralization bias of gods' minds; American Christians who believe that God is omniscient rate God as more knowledgeable of moral behaviors than nonmoral information. Additionally, Tyvans who do not readily report pro- or antisocial behavior among the things that spirit-masters care about will nevertheless rate spirit-masters' knowledge and concern of moral information higher than nonmoral information. However, this knowledge is distributed spatially; the farther away from spirits' place of governance a moral behavior takes place, the less they know and care about it. Finally, the wider the breadth of knowledge Tyvans attribute to spirit-masters, the more they attribute moral concern for behaviors that transpire beyond their jurisdiction. These results further demonstrate that there is a significant gulf between expressed beliefs and intuitive religious cognition and provides evidence for a moralization bias of gods' minds.
An Overview on XML Semantic Disambiguation from Unstructured Text to Semi-Structured Data : Background , Applications , and Ongoing Challenges
(2018)

Since the last two decades, XML has gained momentum as the standard for Web information management and complex data representation. Also, collaboratively built semi-structured information resources, such as Wikipedia, have become prevalent on the Web and can be inherently encoded in XML. Yet most methods for processing XML and semi-structured information handle mainly the syntactic properties of the data, while ignoring the semantics involved. To devise more intelligent applications, one needs to augment syntactic features with machine-readable semantic meaning. This can be achieved through the computational identification of the meaning of data in context, also known as (a.k.a.) automated semantic analysis and disambiguation, which is nowadays one of the main challenges at the core of the Semantic Web. This survey paper provides a concise and comprehensive review of the methods related to XML-based semi-structured semantic analysis and disambiguation. It is made of four logical parts. First, we briefly cover traditional word sense disambiguation methods for processing flat textual data. Second, we describe and categorize disambiguation techniques developed and extended to handle semi-structured and XML data. Third, we describe current and potential application scenarios that can benefit from XML semantic analysis, including: data clustering and semantic-aware indexing, data integration and selective dissemination, semantic-aware and temporal querying, Web and Mobile Services matching and composition, blog and social semantic network analysis, and ontology learning. Fourth, we describe and discuss ongoing challenges and future directions, including: the quantification of semantic ambiguity, expanding XML disambiguation context, combining structure and content, using collaborative/social information sources, integrating explicit and implicit semantic analysis, emphasizing user involvement, and reducing computational complexity.
Thermal characterisation of a copper-clip-bonded IGBT module with double-sided cooling
(2017)

In pursuing higher power density power electronic equipment, the copper clip bonding concept is one of the double-sided cooling approaches under investigation for power semiconductors. This paper presents a comprehensive thermal assessment of the copper clip concept by undertaking comparative studies on one bespoke copper-clip-bonded IGBT power module and a conventional wire-bonded counterpart. A series of single-sided cooling tests and double-sided cooling tests have been undertaken on these two modules. Reductions are reported in the junction to case thermal resistance of up to 23% for one individual die and around 18% for the parallel operation of two dies in the single-sided cooling tests. Whilst in the double-sided cooling experiments, an additional average 18% thermal improvement is achieved due to the addition of a top fan-cooled heatsink mounted onto the copper clip. The results show clearly that the copper clip technology could provide significant benefits in the heat removal for power semiconductors and enable more power dense equipment.
Distributed Detection of Single-Stage Multipoint Cyber Attacks in a Water Treatment Plant
(2016)

A distributed detection method is proposed to detect single stage multi-point (SSMP) attacks on a Cyber Physical System (CPS). Such attacks aim at compromising two or more sensors or actuators at any one stage of a CPS and could totally compromise a controller and prevent it from detecting the attack. However, as demonstrated in this work, using the flow properties of water from one stage to the other, a neighboring controller was found effective in detecting such attacks. The method is based on physical invariants derived for each stage of the CPS from its design. The attack detection effectiveness of the method was evaluated experimentally against an operational water treatment testbed containing 42 sensors and actuators. Results from the experiments point to high effectiveness of the method in detecting a variety of SSMP attacks but also point to its limitations. Distributing the attack detection code among various controllers adds to the scalability of the proposed method.
Feature Extraction With Multiscale Covariance Maps for Hyperspectral Image Classification
(2019)

The classification of hyperspectral images (HSIs) using convolutional neural networks (CNNs) has recently drawn significant attention. However, it is important to address the potential overfitting problems that CNN-based methods suffer when dealing with HSIs. Unlike common natural images, HSIs are essentially three-order tensors which contain two spatial dimensions and one spectral dimension. As a result, exploiting both spatial and spectral information is very important for HSI classification. This paper proposes a new hand-crafted feature extraction method, based on multiscale covariance maps (MCMs), that is specifically aimed at improving the classification of HSIs using CNNs. The proposed method has the following distinctive advantages. First, with the use of covariance maps, the spatial and spectral information of the HSI can be jointly exploited. Each entry in the covariance map stands for the covariance between two different spectral bands within a local spatial window, which can absorb and integrate the two kinds of information (spatial and spectral) in a natural way. Second, by means of our multiscale strategy, each sample can be enhanced with spatial information from different scales, increasing the information conveyed by training samples significantly. To verify the effectiveness of our proposed method, we conduct comprehensive experiments on three widely used hyperspectral data sets, using a classical 2-D CNN (2DCNN) model. Our experimental results demonstrate that the proposed method can indeed increase the robustness of the CNN model. Moreover, the proposed MCMs+2DCNN method exhibits better classification performance than other CNN-based classification strategies and several standard techniques for spectral-spatial classification of HSIs.
Interactions Between Philosophy and Artificial Intelligence: The Role of Intuition and Non-Logical Reasoning in Intelligence
(1971)

This paper echoes, from a philosophical standpoint, the claim of McCarthy and Hayes that Philosophy and Artificial Intelligence have important relations. Philosophical problems about the use of ’intuition’ in reasoning are related, via a concept of analogical representation, to problems in the simulation of perception, problem-solving and the generation of useful sets of possibilities in considering how to act. The requirements for intelligent decision-making proposed by McCarthy and Hayes are criticised as too narrow, and more general requirements are suggested instead.
Low-resource routing attacks against tor
(2007)

Tor has become one of the most popular overlay networks for anonymizing TCP traffic. Its popularity is due in part to its perceived strong anonymity properties and its relatively low latency service. Low latency is achieved through Torâ s ability to balance the traffic load by optimizing Tor router selection to probabilistically favor routers with highbandwidth capabilities.
 We investigate how Torâ s routing optimizations impact its ability to provide strong anonymity. Through experiments conducted on PlanetLab, we show the extent to which routing performance optimizations have left the system vulnerable to end-to-end traffic analysis attacks from non-global adversaries with minimal resources. Further, we demonstrate that entry guards, added to mitigate path disruption attacks, are themselves vulnerable to attack. Finally, we explore solutions to improve Torâ s current routing algorithms and propose alternative routing strategies that prevent some of the routing attacks used in our experiments.
High-Efficient Patch Antenna Array for E-Band Gigabyte Point-to-Point Wireless Services
(2012)

This letter presents a low-cost, high-gain, and high-efficiency 4 × 4 circular patch array antenna for gigabyte point-to-point wireless services at E-band (81-86 GHz). The antenna structure consists of two layers. The feed network is placed at the bottom layer, while the circular patches are on the top layer. To increase the efficiency of the antenna array, substrate integrated waveguide (SIW) is used to feed the circular patches through longitudinal slots etched on the top metallic surface. Low-cost printed circuit board (PCB) process is used to fabricate the antenna prototype. Simulated and measured bandwidths of the antenna array are 7.2%, which covers the desired frequency range of 81-86 GHz. Measured gain of the 4 × 4 antenna array is 18.5 dBi, which is almost constant within the antenna bandwidth. Measured radiation efficiency of 90.3% is obtained.
Robobarista: Learning to Manipulate Novel Objects via Deep Multimodal Embedding
(2016)

There is a large variety of objects and appliances in human environments, such as stoves, coffee dispensers, juice extractors, and so on. It is challenging for a roboticist to program a robot for each of these object types and for each of their instantiations. In this work, we present a novel approach to manipulation planning based on the idea that many household objects share similarly-operated object parts. We formulate the manipulation planning as a structured prediction problem and learn to transfer manipulation strategy across different objects by embedding point-cloud, natural language, and manipulation trajectory data into a shared embedding space using a deep neural network. In order to learn semantically meaningful spaces throughout our network, we introduce a method for pre-training its lower layers for multimodal feature embedding and a method for fine-tuning this embedding space using a loss-based margin. In order to collect a large number of manipulation demonstrations for different objects, we develop a new crowd-sourcing platform called Robobarista. We test our model on our dataset consisting of 116 objects and appliances with 249 parts along with 250 language instructions, for which there are 1225 crowd-sourced manipulation demonstrations. We further show that our robot with our model can even prepare a cup of a latte with appliances it has never seen before.
Big data processing framework of learning weather information and road traffic collision using distributed CEP from CCTV video: Cognitive image processing
(2017)

Nowadays, the many traffic information are extracted from CCTV. The road occupancy, vehicle speed, accident detection, traffic collision and weather information are calculated in CCTV. These are big data comes from varying sources, such as, social sites or mobile phone GPS signals and so on. The Hadoop and HBase can store and analyze these real-time big data in a distributed processing framework. This framework can be designed as flexible and scalable framework using distributed CEP that process massive real-time traffic data and ESB that integrates other services. In this paper, we propose a new architecture for distributed processing that enables big data processing on the road traffic data with specially weather information and its related information analysis. We tested the proposed framework on road traffic data gathering 41 CCTV from Icheon to Gangneung freeway section in Korea. And the forecasting method is introduced at intermediate local area between CCTV.